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检索条件"机构=Computer science and information technology Programs"

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Physician Scheduling using Constraint Satisfaction Programming 11

Physician Scheduling using Constraint Satisfaction Programmi...

引用

11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Bakr, Hend A. Salama, Ahmed M. Fares, Ahmed Zaky, Ahmed B. Cairo University Biomedical Engineering Program Faculty of Engineering Giza Egypt Benha University Computer Systems Engineering Program Faculty of Engineering at Shoubra Banha Egypt Computer science and information technology Programs Alexandria Egypt Benha University On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency. © 2023 IEEE.

关键词： Constraint satisfaction problems

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Improving Diabetes Forecasting: An Ensemble Approach with Feature Selection in Time Series Analysis

Improving Diabetes Forecasting: An Ensemble Approach with Fe...

引用

Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Nour Alhuda Adel Ahmed Safwat Omnia Nabil Ahmed B. Zaky Samir A. Elsagheer 4th year Computer Science and Engineering (CSE) students at Egypt-Japan University of Science and Technology (E-JUST) Computer Science and Information Technology Programs (CSIT) at E-JUST

Addressing the global health challenge of diabetes through the lens of time series analysis, our study leverages machine learning to advance prediction and management. Introducing various models-linear regression, random forest, gradient boosting, elastic net regression, and support vector regression-implemented in Python with a dataset from GitHub, our methodology emphasizes meticulous data preprocessing and feature selection. Among the explored ensemble techniques, the combination of linear regression, random forest, and gradient boosting stands out, achieving a low mean squared error of 16.17. This underscores the potential of our approach to enhance diabetes prediction accuracy and improve management within time series analysis.

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Preliminary Evaluation of Kyokko for Inter-FPGA Communication Framework CIRCUS

Preliminary Evaluation of Kyokko for Inter-FPGA Communicatio...

引用

IEEE International Conference on Cluster Computing Workshops and Posters (CLUSTER WORKSHOPS)

作者： Kaito Kitazume Norihisa Fujita Ryohei Kobayashi Taisuke Boku Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Center for Computational Sciences University of Tsukuba

In recent years, Field-Programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz... 详细信息

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

关键词： Conferences High performance computing Circuits Cluster computing Low latency communication Field programmable gate arrays

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Seagrass Classification Using Differentiable Architecture Search 20

Seagrass Classification Using Differentiable Architecture Se...

引用

20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Ozaeta, Mark Anthony A. Fajardo, Arnel C. Brazas, Felimon P. Cantal, Jed Allan M. Technological Institute of the Philippines-Manila Graduate Programs Metro Manila Philippines College of Computing Studies Information and Communication Technology Isabela State University-Cauayan Isabela Philippines College of Science and Computer Studies De la Salle University-Dasmariñas Cavite Philippines

ISBN: (纸本)9798350300505

Seagrasses are among the most ecologically significant and diverse ecosystems on Earth, playing a crucial role in maintaining the health and productivity of coastal environments. However, these important habitats are threatened by various human activities, including pollution, habitat destruction, and climate change. To address these challenges, it is essential to develop effective conservation and management strategies that protect seagrass ecosystems and the species that depend on them. Accurately identifying various seagrass species is essential to understanding their habitat and overall health. The researchers have developed a seagrass species identification model to address this challenge using a differentiable architecture search with an early stopping strategy. This model achieved an impressive overall accuracy of 93.3% within a relatively short training time of 4 hours and 11 minutes using a commercially-available Apple MacBook device. This model has the potential to greatly improve the efficiency and accuracy of seagrass species identification, providing valuable insights for conservation efforts and supporting the conservation of these vital ecosystems. © 2023 IEEE.

关键词： Ecosystems

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A Novel Inception Deep Learning Model for Mobile AI Stroke Prediction System: AI Research and Industry Perspectives for Connected Health

A Novel Inception Deep Learning Model for Mobile AI Stroke P...

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Computing and Informatics (ICCI), International Conference on

作者： Bassant M. Elbagoury Rytis Maskeliuans Marwa Zaghow Nabil Kamel Faculty of Computer and Information Sciences Faculty of Computer Science and Engineering Ain Shams University Computer Science and Artificial Intelligence Programs King Salman International University KSIU Department of Multimedia Engineering Faculty of Informatics Kaunas University of Technology Lithuania Sales Business and Services Transformation Dell technologies EMEA Egypt Computer Science and Artificial Intelligence Programs Faculty of Computer Science and Engineering King Salma International university KSIU

Artificial intelligence technologies for the mobile health and smart hospitals are important, by applying predictive Analytics and Deep Learning algorithms and developing new models. The main objective is applying artificial Intelligence methods in the medical field, especially for heart/brain stroke diseases diagnosis and prediction for emergency patients' cases. Thus, to save patients' lives, also through the integration with IoT and wearable technologies, which are integrated with AI and DL algorithms that help to make sense of bio signals predictive analytics such as biomedical sensors processing. and complex diseases predictions and early detection like heart and stroke diseases. Also dealing with EMG sensors for stroke prediction. Moreover, Intelligent Mobile Health based on our previously introduced project of AI smart hospital and Mobile AI stroke prediction system for connected health and stroke emergencies. In this paper, a new deep learning model has been built and tested for Stroke EMG signal prediction by modifying the Inception-v3 architecture and other deep learning models. Also, this paper compares current results of Mobile AI health Inception model with our previous developed Mobile AI stroke engine that depends on hybrid LSTM deep learning for EMG signals prediction. Both models have achieved high accuracies. Moreover, the inception model is more stable and higher average accuracies that reaches 98%. Moreover, AI research and future industry implementations for Generative AI Dell Technologies servers is discussed.

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A Web-Based Zakat Recipient Determination System using the Naïve Bayes Algorithm 5

A Web-Based Zakat Recipient Determination System using the N...

引用

5th IEEE International Conference on computer and Informatics Engineering, IC2IE 2022

作者： Kurniawan, Rahmad Nurkholis Ahmad Nazri, Mohd Zakree Lestari, Fitra Salambue, Roni Sukamto Universitas Riau Faculty of Mathematics and Natural Sciences Department of Computer Science Pekanbaru Indonesia Graduate Programs Universitas Putra Indonesia 'YPTK' Padang Department of Computer Science Padang Indonesia Faculty of Information Science and Technology Selangor Bangi Malaysia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Industrial Engineering Pekanbaru Indonesia

ISBN: (数字)9781665453387

ISBN: (纸本)9781665453387

As the third pillar of Islam, zakat must be fulfilled by Muslims who have met the requirements of Islamic law. In Indonesia, zakat is managed by by the National Amil Zakat Agency (BAZNAS). BAZNAS is obliged to distribute zakat to eligible people who call in Islamic is Mustahiq to receive zakat according to the applicable category rules. Mustahiq (beneficiaries) are groups entitled to receive zakat according to the priority scale arranged in the Zakat program. As a case study, BAZNAS in Pekanbaru city determines the recipient of zakat manually by interviewing each candidate. Factors influencing decisions, such as feelings, emotions, sentiments, and moods, can lead to wrong decisions. Human weakness contrasts with the advantages of artificial intelligence. Therefore, this study aimed to design and build a web-based application system to determine the category of the recipient of zakat automatically and quickly using the Naïve Bayes algorithm. A total of 3222 data in 2015-2017 were obtained in BAZNAS Pekanbaru for training data. Based on the experimental testing results, the highest accuracy was 93.7%, and F-measure was 96.7% in 80% of training data. Based on the test results, it can be concluded that web-based applications using the Naïve Bayes method has the potential to be used in determining the recipient of zakat in BAZNAS Pekanbaru. © 2022 IEEE.

关键词： Websites

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Enhanced Pooling Technique in Convolutional Neural Networks Model for Classification of Magnoliophyta Plant DNA Barcodes

Enhanced Pooling Technique in Convolutional Neural Networks ...

引用

Big Data Analytics and Practices (IBDAP), International Conference on

作者： Lilibeth P. Coronel Arnel C. Fajardo Ruji P. Medina Graduate Programs Technological Institute of the Philippines Quezon City Philippines Mindanao State University at Naawan Philippines College of Computer Science Information and Communication Technology Isabela State University Cauayan Philippines Graduate Programs Technological Institute of the Philippines - Quezon City Quezon City Philippines

ISBN: (数字)9798350391749

ISBN: (纸本)9798350391756

Identifying plant Deoxyribonucleic acid (DNA) barcodes has gained substantial importance for biodiversity conservation and understanding evolutionary relationships. However, classifying these species remains challenging due to the complex nature of the DNA sequences. Convolutional Neural Networks (CNNs) have proven effective for pattern recognition tasks in DNA sequence classification, however, standard pooling techniques often result in the loss of important information. This study introduced a modified CNN model with an enhanced pooling technique called Horizontal Sequence Pooling, designed to retain important features and improve accuracy. The method is applied to classify the Magnoliophyta plant DNA barcodes of 75 genera and evaluated against the CNN model with standard max pooling and average pooling techniques. Results show that the proposed technique achieved the highest accuracy of 0.9801, the lowest validation loss of 0.0393, superior Area Under Curve – Precision and Recall (AUC-PR) of 0.9979, and Matthews Correlation Coefficient (MCC) of 0.9814, respectively, outperformed standard pooling techniques. These results indicate that Horizontal Sequence Pooling effectively extracts relevant features from DNA sequences, enhancing the classification accuracy, precision, recall, and balance across both positive and negative classes demonstrating its robustness in handling imbalanced datasets.

关键词： Correlation coefficient Accuracy DNA Feature extraction Robustness Data models Pattern recognition

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Hybrid Machine Learning Classification for Tipple Negative Breast Cancer

Hybrid Machine Learning Classification for Tipple Negative B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Amira M. Abozaid Ahmed B. Zaky Biomedical Engineering Department Cairo University Giza Egypt Computer Science and Information Technology Programs (CSIT) Egypt Japan University of Science and Technology Molecular Biology division Teaching assistant at Faculty of Science Alexandria University Faculty of Engineering at Shubra Benha University Egypt

This research introduces an innovative approach to dissecting Triple-Negative Breast Cancer (TNBC) by integrating count-based RNA analysis and machine learning techniques. Through the strategic amalgamation of K-means clustering, Convolutional Neural Network (CNN), and Support Vector Machine (SVM), this study transcends conventional classification methods, revealing detailed molecular insights within the complex TNBC landscape. The methodology initiates with count-based RNA analysis for dimensionality reduction, refining molecular classifications through K-means clustering. The incorporation of CNN allows for nuanced feature extraction, capturing intricate genomic relationships, while SVM enhances subtype predictions with precise classification. The addition of Gene Ontology (GO) and pathway analyses enriches our understanding by unraveling the functional implications of identified gene clusters. This synergistic methodology not only advances our comprehension of TNBC subtypes but also establishes a groundwork for targeted therapeutic strategies. The fusion of count-based RNA analysis and machine learning enables a comprehensive and nuanced exploration of TNBC complexity, offering valuable insights for personalized oncology approaches in the era of precision medicine

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Deep Learning Approach for Seagrass Species Classification

Deep Learning Approach for Seagrass Species Classification

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Green Energy, Computing and Sustainable technology (GECOST), International Conference on

作者： Mark Anthony A. Ozaeta Arnel C. Fajardo Felimon P. Brazas Jed Allan M. Cantal Graduate Programs Technological Institute of the Philippines-Manila Metro Manila Philippines College of Computing Studies Information and Communication Technology Isabela State University-Cauayan Isabela Philippines College of Science and Computer Studies De La Salle University-Dasmari ñas Cavite Philippines

Integrating deep learning algorithms with image-based data presents considerable potential for improving classification tasks, specifically in the context of seagrass monitoring and conservation initiatives. As exceptionally productive ecosystems, seagrasses encounter significant risks from climatic fluctuations and human activities. Furthermore, careful monitoring and conservation approaches are imperative. However, the intricate characteristics of seagrass species present obstacles to developing robust classification models. This research presents a novel methodology for enhancing the identification of seagrass species by curating an extensive image dataset that includes temporal variations, a wide range of geographic locations, water depths, and turbidity levels. A deep learning approach based on differentiable architecture search with early stopping was used to train a neural network. The results showed an impressive 93.72% in classifying five seagrass species sampled in Calatagan, Batangas and Puerto Galera, Oriental Mindoro, Philippines namely, Cymodocea rotundata, Enhalus acoroides, Halophila ovalis, Syringodium isoetifolium, and Thalassia hemprichii.

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Physician Scheduling using Constraint Satisfaction Programming

Physician Scheduling using Constraint Satisfaction Programmi...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Ahmed M. Salama Ahmed Fares Ahmed B. Zaky Biomedical Engineering Program Faculty of Engineering Cairo University Giza Egypt Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Banha Egypt Computer science and information technology Programs Egypt-Japan University of Science and Technology (E-JUST) Alexandria Egypt On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University

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